Neural Network Tutorials - Herong's Tutorial Examples - v1.22, by Herong Yang
MSE (Mean Squared Error)
This section describes MSE (Mean Squared Error) as a metric to evaluate the performance of a continuous prediction model.
What Is MSE (Mean Squared Error)? - MSE is a commonly used metric to evaluate the performance of a continuous prediction model. It takes the average value of squared errors of all samples.
Given a prediction model and a set of test samples, the MSE of the model on the test set is defined below:
where:
Obviously, if MSE = 0, the model is 100% accurate on the test set.
Table of Contents
Deep Playground for Classical Neural Networks
Building Neural Networks with Python
Simple Example of Neural Networks
TensorFlow - Machine Learning Platform
PyTorch - Machine Learning Platform
CNN (Convolutional Neural Network)
RNN (Recurrent Neural Network)
GAN (Generative Adversarial Network)
►Performance Evaluation Metrics